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Found 7 papers, 4 papers with code
Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling
Addressing this, we introduce the Recency-Weighted Temporally-Segmented (ReWTS, pronounced `roots') ensemble model, a novel chunk-based approach for multi-step forecasting.
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving.
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments
Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions.
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Its high computational complexity results in high power consumption from the control algorithm, which could account for a significant share of the energy resources in battery-powered embedded systems.
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensuring safe operation.
Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning
In control applications there is often a compromise that needs to be made with regards to the complexity and performance of the controller and the computational resources that are available.
Accelerating Reinforcement Learning with Suboptimal Guidance
Reinforcement Learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less random fashion for any learning signals.
